1. The Field of the Invention
This invention relates to signal processing and, more particularly, to novel systems and methods for pattern recognition and data interpretation relative to monitoring and categorizing patterns for predictably detecting and quantifying hydrocarbon deposits.
2. The Background Art
Seismic waves have been used to generate models of the earth's composition. In more recent times, seismic waves have been employed in an effort to located resources such as oil and gas deposits within the earth's surface formations. Well log data has also been applied to predicting resources within the earth. However, because of the low signal-to-noise ration (SNR) or high noise-to-signal ratio and complexity of seismic waves and well log data, generating accurate models of resource deposits has been difficult.
To facilitate the extraction of useful information, various types of signal processing strategies have been applied to seismic waves and well log data. Analysis strategies used by those skilled in the art have included spectral analysis, seismic trace stacking, various transforms, time-frequency distributions, spatial filtering methods, neural networks, fuzzy logic systems, and integrated neurofuzzy systems. As appreciated, each of these analysis techniques, however, typically relies on human inspection of the generated waveforms. Visual inspection may miss vital content that is implicit or hidden (e.g. time domain information).
Stacking of multiple seismic traces from pre-stack gathers generally employs summing or averaging signals acquired over many angles of incidence and many offsets. The end goal of stacking is to reduce noise and amplify certain, useful, seismic, waveforms. However, it further obscures other data. While useful for certain applications, averaging and stacking techniques have several significant drawbacks. Large quantities of information, just as valuable but less understood, are lost in the averaging or stacking. Only selected types of signals are able to survive massive averaging or summation over multiple offsets. Moreover, the averaging process only provides a comparison between groups of offsets or groups of angles rather than between the individual offsets or angles.
Alternative analysis approaches including Fourier Transforms; Hilbert Transforms; Wavelet Transforms; Short-Time Fourier Transforms; Wigner Functions; Generalized Time-Frequency Distributions; Parameter vs. Offset (PVO); and Amplitude vs. Offset (AVO) have been applied to seismic waves and well log data. While valuable for certain applications, these approaches typically require averaging over small groups of angles or small groups of offsets. Moreover, these approaches have not been fully integrated with computerized condition discrimination. Like spectral analysis techniques, these approaches rely on visual inspection of the generated waveforms, greatly increasing the possibility of error.
Spatial filtering methods, including: Principal Component Analysis; Singular Value Decomposition; and Eigenvalue Analysis have been applied to seismic waves and well log data. Such filtering methods tend to ignore frequency and temporal information. Additionally, these filtering techniques are usually applied only to seismic traces that have been averaged (post-stack seismic traces), otherwise the noise level is prohibitive.
Additional analysis techniques and methodology have been developed by those skilled in the art, to take advantage of recent increases in computer processing power. Neural networks have been developed to discover discriminate information. The traditional neural network approaches, however, generally take a long time to program and learn, are difficult to train, and tend to focus on local minima to the detriment of other more global and important areas. Moreover, most of these analysis techniques are limited by a lack of integration with time, frequency, and spatial analysis techniques.
Due to their inherent narrow ranges of applicability, prior methods of analysis have provided a fragmentary approach to seismic waves and well log data analysis. What is needed is an integrated waveform analysis method capable of extracting useful information from highly complex and irregular waveforms such as raw seismic data, pre-stack seismic gathers, post-stack seismic traces, and the variety of signal types comprising well log data sets.